How to do Cohort Analysis?

May 23, 2015

Recently, Google has included a new feature named as cohort analysis into its analytics package. The new feature is very helpful for online marketers to determine trends and user behavior that are based on time. It can be used for the purpose of testing of various products, advertisement, content, forms etc.

What is cohort?

The cluster of individuals, which has communal characteristic or behavior within a pre-defined period of time, is called the cohort. For example, all the first time visitors of a website on May 4, 2015 or the people who purchased a specific product on a particular date could be considered as a cohort because of their shared characteristics.

A Cohort is a time based segmentation that helps marketers to analyze the delayed effect and user behavior. The basic difference between other segmentation and the cohort is that the cohort is highly based on dates or a particular period of time.

Before the inclusion of this feature it was very difficult to evaluate clustering of data acquisition, but now it can be easily done through cohort analysis.

How to use cohort analysis

After knowing the basis of cohort, it is time to know how to use it and what more you can do with a cohort analysis. To understand it more take a look at the example below:

In this picture you can see that on April 27, 2015, 126 unique users visited the website, this data represent day 0. Now if you look at day 1 in the third column of the third row, then you can see the percentage of those 126 visitors who visited the website again which is 2.38%. It means that on April 28, 2015, 2.38% visitors visited the website again and the same amount of visitors (2.38%) visited on April 29, 2015. To check the further ratio of visitors, you can follow the same pattern for next rows and find out how many of 136 unique visitors visited your website on April 29, April 30th and May 1 and so on.

The analysis of this cohort data gives a great insight of a website’s user retention rate. That means it can tell you how many visitors are showing interest in paying a second visit to your website. Also, this data set helps you to find out where your site lacks and what actions you need to take to improve the retention rate of your website.

How to configure cohort analysis

The configuration of the cohort is based on these four:

Cohort type: Currently, cohort analysis in running in beta mode and it only let you access the acquisition date, therefore you can find out users who visited your website on a specific date or how they interacted with your website within a certain period of time.

Cohort size: Cohort size can be set by days, weeks, or even months to further analyze the data. Cohort size lets you see the set of visitors that visited in February and again visited in March month. When you choose the cohort size you can pick the range of date of 7, 14, 21 or 30 days according to the need. It might also be possible that Google will introduce some more cohort sizes to analyze more dimensions.

Metric: Metrics can be measured on the basis of per user, app views, user retention, conversion, goal completion, page views, sessions per visitors and more. User retention is one of the most important factors as it can help you to determine the success of your website/app.

If you want to perform cohort analysis on a variety of segments, then you can do that too. For example, if you want to see average session time on mobile device & desktop device then you can do that efficiently.

Cohort analysis for mobile apps

You have seen that cohort analysis can be done for websites, but it is also possible to do it for mobile applications. Since the number of mobile users is growing very fast, it becomes important to scrutinize the behavior of users on your mobile applications.

There are some questions you might want to know the answers such as, how frequently an app is opened by a user, how long does a user stay on your app, how engaging your mobile application is. The answer to all these questions is the cohort analysis. After analysis, you’ll be able to make good improvements in your strategy that increase your mobile app performance.

Here is an example that illustrates how cohort analysis works for mobile apps.